Laboratory for Information and Decision Systems (LIDS)Laboratory for Information and Decision Systems at MIThttp://hdl.handle.net/1721.1/17752018-03-06T19:56:14Z2018-03-06T19:56:14ZSimulation of two methods in co-adaptive control for brain-machine interfacesKowalski, KevinSrinivasan, Lakshminarayanhttp://hdl.handle.net/1721.1/709752012-06-01T13:04:29Z2012-06-01T00:00:00ZSimulation of two methods in co-adaptive control for brain-machine interfaces
Kowalski, Kevin; Srinivasan, Lakshminarayan
Simulation of two methods in co-adaptive control for brain-machine interfaces: cursorGoal (developed by the Shenoy Lab @ Stanford EE and the Carmena Lab @ UC Berkeley EE), and Joint RSE (developed by the Neural Signal Processing Laboratory, www.nsplab.org). The healthy volunteer represents a sensorimotor neural control network. His arm movements are captured with the Microsoft Kinect and used to drive simulated neural activity (not shown) from a point process model of primary motor cortex. This neural activity determines movements of the on-screen cursor through a brain-machine interface (BMI) algorithm. In all trials, the cursor begins at a random point on the outer circle, and the user attempts to adjust his arm movements to bring the cursor to the inner circle (target) for a specified hold period of 0.5 sec. Maximum allowed trial time is 3 sec. In the training trials of these simulations, the various BMI algorithms must both learn neural signal parameters and decode arm movement. In test trials, the neural signal parameters are fixed, and both methods use an identical filter formulation (Eden, 2004 Neural Computation) with a random walk state equation to drive arm movements. cursorGoal and Joint RSE differ markedly in the way visual feedback to the user (cursor movement) is determined during training trials, as well as in the procedure for learning neural signal parameters. The related manuscript delineates the relative contributions of these algorithmic variations to the differing performance of these co-adaptive BMI control methods, where Joint RSE consistently and substantially outperforms cursorGoal.
2012-06-01T00:00:00ZMatlab-Kinect Interface CodeKowalski, KevinSrinivasan, Lakshminarayanhttp://hdl.handle.net/1721.1/709742012-06-01T12:39:42Z2012-06-01T00:00:00ZMatlab-Kinect Interface Code
Kowalski, Kevin; Srinivasan, Lakshminarayan
This .zip file contains code and installation instructions for acquiring 3d arm movements in Matlab using the Microsoft Kinect 3d camera. The provided code has been validated in 32-bit and 64-bit Matlab with 32-bit and 64-bit Windows 7 respectively.
2012-06-01T00:00:00ZSupplementary Movie: Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devicesSrinivasan, Lakshminarayanda Silva, Marcohttp://hdl.handle.net/1721.1/602982010-12-16T07:06:15Z2010-12-15T00:00:00ZSupplementary Movie: Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devices
Srinivasan, Lakshminarayan; da Silva, Marco
This supplementary movie demonstrates three neural prosthetic algorithms in the simulated control of an overactuated 3-dimensional virtual robotic arm with a real-time inverse kinematics engine. Specifically, this movie compares the ability of these algorithms to generate movements with variously paced arrival times. Paper Abstract: We routinely generate reaching arm movements to function independently. For paralyzed users of upper-extremity neural prosthetic devices, flexible, high-performance reaching algorithms will be critical to restoring quality-of-life. Previously, algorithms called real-time reach state equations (RSE) were developed to integrate the user’s plan and execution-related neural activity to drive reaching movements to arbitrary targets. Preliminary validation under restricted conditions suggested that RSE might yield dramatic performance improvements. Unfortunately, real-world applications of RSE have been impeded because the RSE assume a fixed, known arrival time. Recent animal-based prototypes attempted to break the fixed-arrival time assumption by proposing a Standard Model (SM) that instead restricted the user’s movements to a fixed, known set of targets. Here, we leverage General Purpose Filter Design (GPFD) to break both of these critical restrictions, freeing the paralyzed user to make reaching movements to arbitrary target sets with various arrival times and definitive stopping. In silico validation predicts that the new approach, GPFD-RSE, outperforms the SM while offering greater flexibility. We demonstrate GPFD-RSE against SM in the simulated control of an overactuated 3-dimensional virtual robotic arm with a real-time inverse kinematics engine.
Supplementary movie for IEEE Transactions in Biomedical Engineering paper, "Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devices", posted December 2010.
2010-12-15T00:00:00ZStochastic Optimal Control: The Discrete-TIme CaseBertsekas, Dimitir P.Shreve, Stevenhttp://hdl.handle.net/1721.1/48522006-10-14T00:06:32Z2004-03-03T21:32:23ZStochastic Optimal Control: The Discrete-TIme Case
Bertsekas, Dimitir P.; Shreve, Steven
2004-03-03T21:32:23Z